CyberTraining: CIU: SJSU Data Science for All Seminar Series

网络培训：CIU：SJSU 全民数据科学研讨会系列

• 批准号: 1829622
• 负责人: Leslie Albert
• 金额: $ 41.01万
• 依托单位: San Jose State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1829622&HistoricalAwards=false
• 关键词: CyberTraining; CIU; SJSU; Data; Science

The Nation's research enterprise faces a shortage of data scientists. Expanding the pipeline of data science students, particularly from underrepresented populations, requires educational institutions to increase awareness of data science and inspire a passion for data in students as they begin their academic careers. Currently, few community colleges or undergraduate programs provide training in cyberinfrastructure tools or data science techniques to a broad student population. This project takes a novel approach to augmenting the Nation's data science workforce by training community college and undergraduate students to provide data analytics support to data scientists through a series of "Data Science for All" extracurricular seminars. The seminars require no prior data science knowledge, emphasize transferable skills, and present a feasible path into data science-related research and other careers for students from a broad array of disciplines and from underrepresented groups without extending their time to graduation. By increasing the Nation's data science capabilities and the diversity of its data science research workforce, the project serves the national interest, as stated by NSF's mission: to promote progress of science and advance the prosperity and welfare of the Nation. The goals of this project are to increase undergraduate student awareness of data-driven science and to grow and diversify the population of students trained to perform data wrangling - the data acquisition, transformation, cleaning, and profiling required to prepare data for analysis. According to industry experts, data wrangling is the "heavy lifting" of data science, constituting up to 80% of a data scientist's daily work. Shifting this time-consuming effort to trained data analysts free data scientists to focus more of their time on research. The project achieves its goals through the development and delivery of widely consumable, extracurricular seminars providing interactive training on data science concepts and industry-leading data wrangling tools to undergraduate and community college students. Initial seminar topics, selected in collaboration with the project's advisory board, include Python, Jupyter notebooks, Apache Spark, Tableau, and demystifying artificial intelligence (AI). The seminars' focus on data wrangling also introduces students to data preparation documentation - capturing the data provenance needed for reproducible science. This project's contribution to the Nation's data science workforce is broadened through the free and open distribution of its seminar materials and supplemental resources and its online instructor support community. To encourage adoption at Bay Area community colleges and universities, instructor training is provided through co-instruction and a teaching-the-teacher model. The project contributes to pedagogical research by identifying instructional approaches most effective in teaching data science to a diverse population of undergraduate students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

美国的研究企业面临着数据科学家短缺的问题。扩大数据科学学生的渠道，特别是来自代表性不足的人群，要求教育机构提高数据科学的意识，并在学生开始他们的学术生涯时激发他们对数据的热情。目前，很少有社区学院或本科课程向广大学生提供网络基础设施工具或数据科学技术方面的培训。该项目采用了一种新的方法来增加国家的数据科学劳动力，通过培训社区学院和本科生通过一系列的“全民数据科学”课外研讨会为数据科学家提供数据分析支持。这些研讨会不需要先前的数据科学知识，强调可转移的技能，并为来自广泛学科和代表性不足群体的学生提供了一条进入与数据科学相关的研究和其他职业生涯的可行途径，而不会延长他们的毕业时间。通过提高国家的数据科学能力和数据科学研究队伍的多样性，该项目符合国家利益，正如NSF的使命所述：促进科学进步，促进国家的繁荣和福利。该项目的目标是提高本科生对数据驱动型科学的认识，并使接受过数据辩论培训的学生群体增长和多样化--数据辩论是准备数据进行分析所需的数据获取、转换、清理和分析。据业内专家介绍，数据角力是数据科学的一项重担，高达数据科学家日常工作的80%。将这种耗时的工作转移到训练有素的数据分析师手中，数据科学家可以自由地将更多时间集中在研究上。该项目通过开发和提供可广泛使用的课外研讨会来实现其目标，该研讨会为本科生和社区大学生提供关于数据科学概念和行业领先的数据辩论工具的互动培训。最初的研讨会主题是与该项目的顾问委员会合作挑选的，包括Python、Jupyter笔记本、阿帕奇Spark、Tableau和揭开人工智能(AI)的神秘面纱。这些研讨会的重点是数据争论，也向学生介绍了数据准备文档--捕捉可重现科学所需的数据来源。该项目通过免费和开放地分发其研讨会材料和补充资源以及其在线教师支持社区，扩大了该项目对国家数据科学劳动力的贡献。为了鼓励湾区社区学院和大学采用这一模式，教师培训通过联合授课和教师讲授的模式提供。该项目通过确定在向不同群体的本科生教授数据科学方面最有效的教学方法，为教学研究做出贡献。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Data Science for All: Apache Spark & Jupyter Notebooks

全民数据科学：Apache Spark

• 发表时间: 2021
• 期刊: Pre-ICIS SIGDSA Symposium on Analytics and AI for a Sustainable and Resilient Future
• 作者: Jensen, Scott;Albert, Leslie;Huerta, Esperanza
• 通讯作者: Huerta, Esperanza

Python Foundations: Data Science for All

Python 基础：全民数据科学

• 发表时间: 2019
• 期刊: International Conference on Information Systems
• 作者: Albert, Leslie;Huerta, Esperanza;Jensen, Scott
• 通讯作者: Jensen, Scott